Protecting image features in stylized representations of a source image

ABSTRACT

Systems and methods herein describe an image stylization system. The image stylization system accesses a set of images corresponding to a target domain style, generates a set of paired images using a first machine learning model, analyze the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria, determines a set of image transformations for the generated set of pairs, generates a transformed set of paired images by performing the set of image transformations on the set of paired images, and generates stylized images corresponding to the target domain style using a supervised image translation model trained on the transformed set of paired images.

CLAIM OF PRIORITY

This application claims the benefit of priority to U.S. Provisional Application Ser. No. 63/295,394, filed Dec. 30, 2021, which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

Embodiments herein generally relate to generating stylized representation of a source image. More specifically, but not by way of limitation, embodiments herein describe protecting desired image features when generating a stylized representation of a source image.

BACKGROUND

The increasing popularity of social media applications have attracted billions of users around the world. Creators of such media applications thus have an incentive to appeal to users across a wide spectrum of race, gender, ethnicity, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. Some nonlimiting examples are illustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment in which the present disclosure may be deployed, in accordance with some examples.

FIG. 2 is a diagrammatic representation of a messaging system, in accordance with some examples, that has both client-side and server-side functionality.

FIG. 3 is a diagrammatic representation of a data structure as maintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance with some examples.

FIG. 5 is a flowchart for an access-limiting process, in accordance with some examples.

FIG. 6 is a block diagram of an image stylization system, according to example embodiments.

FIG. 7 illustrates target domain styles, according to example embodiments.

FIGS. 8-14 illustrate stylized images generated using an image stylization system, according to example embodiments.

FIG. 15 is a flowchart of a method for generating stylized images, according to example embodiments.

FIG. 16 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, in accordance with some examples.

FIG. 17 is a block diagram showing a software architecture within which examples may be implemented.

DETAILED DESCRIPTION

Systems and methods herein resolve the technical problem of protecting (e.g., preserving, enhancing) desired image features when generating a stylized representation of a source image. A stylized representation of a source image is a modified version of the source image that depicts the source image in a target domain style. For example, a stylized representation of a source image depicting a human user may be modified to depict the human user in an animation style wherein the human user is depicted as an animation.

One challenge with generating stylized representations of a source image is preserving certain features of the source image during the stylization process. For example, certain features of a source image, such as the skin tone of a user, lip color, nose appearance, object texture etc., may be diluted or generalized during the transformation into the stylized representation. These features may therefore not be accurately represented in the resulting stylized representations. In some cases, preserving these features may be desirable to preserve important features during the stylization process. For example, preserving certain features, such as skin tone, may be desirable in certain applications to accurately represent a diverse user group. As another example, enhancing features, such as the color or texture of objects, may be desirable to accurately represent the object. Accordingly, there is a need for an image stylization system that preserves selected features during face stylization.

The described systems and methods alleviate the issues with previous solutions by providing for selected ‘protected features’ to be preserved when generating stylizing representations of a source image. The paragraphs below describe an image stylization system that preserves selected protected features during transformation of an image to a target domain style. A protected feature may be any feature of an image that is selected to be preserved during transformation of an image to a target domain style. For example, a protected feature may be a facial feature that a user associates with their identity, such as skin tone. In this type of example, a user with a darker skin tone may wish to have their skin tone accurately represented in a stylized representation as an anime character (e.g., the target domain style). The image stylization system may be configured to protect the feature of skin tone such that the skin tone of a user depicted in a source image is preserved and/or enhanced during the stylization process. As a result, the skin tone of the user will more accurately be represented in the resulting stylized representation of the image.

The described image stylization system consists of a five-staged pipeline. Each stage in the pipeline is performed using one or more sub-systems. In the first stage, the image stylization system uses an image collector system. The image collector system collects sample images of the target domain style.

In the second stage, the image stylization system uses a machine learning (ML) model. The machine learning model generates paired images. The paired images include a first image that is a source image and a second image that is a stylized version of the source image according to a target domain style.

In the third stage, the image stylization system uses a dataset adjustment system. The dataset adjustment system analyzes the set of paired images generated by the unsupervised machine learning model against a plurality of protected feature criteria. The dataset adjustment system generates an adjusted set of images by augmenting the accessed set of images corresponding to the target domain style with a set of new images corresponding to the target domain style based on the protected feature criteria.

The image stylization system may repeat the second stage using the images generated at the third stage. For example, the image stylization system may repeat stage two and regenerate paired images that previously did not meet the protected feature criteria. The step-by-step augmentation of the dataset during stage three allows the image stylization system to train the unsupervised machine learning model that preserves protected features for most image pairs. The image stylization system may repeat stage two and stage three until all the protected feature criteria have been met.

In the fourth stage, the image stylization system uses an image transformation system. Based on the analysis of the protected feature criteria during the third stage, the image transformation system performs transformations on the images generated at the third stage.

In the fifth stage, the image stylization system uses a supervised image translation model. Specifically, the image stylization system trains a supervised image translation model on the adjusted set of paired images generated during the fourth stage. The supervised image translation model is thus trained to generate a stylized face image from an input face image. The stylized face image may correspond to a style from a target domain. Further details of the image stylization system are described in the paragraphs below.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 for exchanging data (e.g., messages and associated content) over a network. The messaging system 100 includes multiple instances of a client device 102, each of which hosts a number of applications, including a messaging client 104 and other applications 106. Each messaging client 104 is communicatively coupled to other instances of the messaging client 104 (e.g., hosted on respective other client devices 102), a messaging server system 108 and third-party servers 110 via a network 112 (e.g., the Internet). A messaging client 104 can also communicate with locally-hosted applications 106 using Applications Program Interfaces (APIs).

A messaging client 104 is able to communicate and exchange data with other messaging clients 104 and with the messaging server system 108 via the network 112. The data exchanged between messaging clients 104, and between a messaging client 104 and the messaging server system 108, includes functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality via the network 112 to a particular messaging client 104. While certain functions of the messaging system 100 are described herein as being performed by either a messaging client 104 or by the messaging server system 108, the location of certain functionality either within the messaging client 104 or the messaging server system 108 may be a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the messaging server system 108 but to later migrate this technology and functionality to the messaging client 104 where a client device 102 has sufficient processing capacity.

The messaging server system 108 supports various services and operations that are provided to the messaging client 104. Such operations include transmitting data to, receiving data from, and processing data generated by the messaging client 104. This data may include message content, client device information, geolocation information, media augmentation and overlays, message content persistence conditions, social network information, and live event information, as examples. Data exchanges within the messaging system 100 are invoked and controlled through functions available via user interfaces (UIs) of the messaging client 104.

Turning now specifically to the messaging server system 108, an Application Program Interface (API) server 116 is coupled to, and provides a programmatic interface to, application servers 114. The application servers 114 are communicatively coupled to a database server 120, which facilitates access to a database 126 that stores data associated with messages processed by the application servers 114. Similarly, a web server 128 is coupled to the application servers 114 and provides web-based interfaces to the application servers 114. To this end, the web server 128 processes incoming network requests over the Hypertext Transfer Protocol (HTTP) and several other related protocols.

The Application Program Interface (API) server 116 receives and transmits message data (e.g., commands and message payloads) between the client device 102 and the application servers 114. Specifically, the Application Program Interface (API) server 116 provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client 104 in order to invoke functionality of the application servers 114. The Application Program Interface (API) server 116 exposes various functions supported by the application servers 114, including account registration, login functionality, the sending of messages, via the application servers 114, from a particular messaging client 104 to another messaging client 104, the sending of media files (e.g., images or video) from a messaging client 104 to a messaging server 118, and for possible access by another messaging client 104, the settings of a collection of media data (e.g., story), the retrieval of a list of friends of a user of a client device 102, the retrieval of such collections, the retrieval of messages and content, the addition and deletion of entities (e.g., friends) to an entity graph (e.g., a social graph), the location of friends within a social graph, and opening an application event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications and subsystems, including for example a messaging server 118, an image processing server 122, a social network server 124, and an image stylization system 130. The messaging server 118 implements a number of message processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., textual and multimedia content) included in messages received from multiple instances of the messaging client 104. As will be described in further detail, the text and media content from multiple sources may be aggregated into collections of content (e.g., called stories or galleries). These collections are then made available to the messaging client 104. Other processor and memory intensive processing of data may also be performed server-side by the messaging server 118, in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122 that is dedicated to performing various image processing operations, typically with respect to images or video within the payload of a message sent from or received at the messaging server 118.

The social network server 124 supports various social networking functions and services and makes these functions and services available to the messaging server 118. To this end, the social network server 124 maintains and accesses an entity graph 308 (as shown in FIG. 3 ) within the database 126. Examples of functions and services supported by the social network server 124 include the identification of other users of the messaging system 100 with which a particular user has relationships or is “following,” and also the identification of other entities and interests of a particular user.

The image stylization system 130 allows for select facial features to be protected during the face stylization process. Specifically, the image stylization preserves protected user features during transformation of a human face image to a target domain style. For example, the image stylization system 130 improves face stylization fairness for diverse user groups.

Returning to the messaging client 104, features and functions of an external resource (e.g., an application 106 or applet) are made available to a user via an interface of the messaging client 104. In this context, “external” refers to the fact that the application 106 or applet is external to the messaging client 104. The external resource is often provided by a third party but may also be provided by the creator or provider of the messaging client 104. The messaging client 104 receives a user selection of an option to launch or access features of such an external resource. The external resource may be the application 106 installed on the client device 102 (e.g., a “native app”), or a small-scale version of the application (e.g., an “applet”) that is hosted on the client device 102 or remote of the client device 102 (e.g., on third-party servers 110). The small-scale version of the application includes a subset of features and functions of the application (e.g., the full-scale, native version of the application) and is implemented using a markup-language document. In one example, the small-scale version of the application (e.g., an “applet”) is a web-based, markup-language version of the application and is embedded in the messaging client 104. In addition to using markup-language documents (e.g., a .*ml file), an applet may incorporate a scripting language (e.g., a .*js file or a .json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch or access features of the external resource, the messaging client 104 determines whether the selected external resource is a web-based external resource or a locally installed application 106. In some cases, applications 106 that are locally installed on the client device 102 can be launched independently of and separately from the messaging client 104, such as by selecting an icon, corresponding to the application 106, on a home screen of the client device 102. Small-scale versions of such applications can be launched or accessed via the messaging client 104 and, in some examples, no or limited portions of the small-scale application can be accessed outside of the messaging client 104. The small-scale application can be launched by the messaging client 104 receiving from a third-party server 110 for example, a markup-language document associated with the small-scale application and processing such a document.

In response to determining that the external resource is a locally-installed application 106, the messaging client 104 instructs the client device 102 to launch the external resource by executing locally-stored code corresponding to the external resource. In response to determining that the external resource is a web-based resource, the messaging client 104 communicates with the third-party servers 110 (for example) to obtain a markup-language document corresponding to the selected external resource. The messaging client 104 then processes the obtained markup-language document to present the web-based external resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, or other users related to such a user (e.g., “friends”), of activity taking place in one or more external resources. For example, the messaging client 104 can provide participants in a conversation (e.g., a chat session) in the messaging client 104 with notifications relating to the current or recent use of an external resource by one or more members of a group of users. One or more users can be invited to join in an active external resource or to launch a recently-used but currently inactive (in the group of friends) external resource. The external resource can provide participants in a conversation, each using respective messaging clients 104, with the ability to share an item, status, state, or location in an external resource with one or more members of a group of users into a chat session. The shared item may be an interactive chat card with which members of the chat can interact, for example, to launch the corresponding external resource, view specific information within the external resource, or take the member of the chat to a specific location or state within the external resource. Within a given external resource, response messages can be sent to users on the messaging client 104. The external resource can selectively include different media items in the responses, based on a current context of the external resource.

The messaging client 104 can present a list of the available external resources (e.g., applications 106 or applets) to a user to launch or access a given external resource. This list can be presented in a context-sensitive menu. For example, the icons representing different ones of the application 106 (or applets) can vary based on how the menu is launched by the user (e.g., from a conversation interface or from a non-conversation interface).

System Architecture

FIG. 2 is a block diagram illustrating further details regarding the messaging system 100, according to some examples. Specifically, the messaging system 100 is shown to comprise the messaging client 104 and the application servers 114. The messaging system 100 embodies a number of subsystems, which are supported on the client-side by the messaging client 104 and on the server-side by the application servers 114. These subsystems include, for example, an ephemeral timer system 202, a collection management system 204, an augmentation system 208, a map system 210, a game system 212, an external resource system 214, and an image stylization system 130.

The ephemeral timer system 202 is responsible for enforcing the temporary or time-limited access to content by the messaging client 104 and the messaging server 118. The ephemeral timer system 202 incorporates a number of timers that, based on duration and display parameters associated with a message, or collection of messages (e.g., a story), selectively enable access (e.g., for presentation and display) to messages and associated content via the messaging client 104. Further details regarding the operation of the ephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managing sets or collections of media (e.g., collections of text, image video, and audio data). A collection of content (e.g., messages, including images, video, text, and audio) may be organized into an “event gallery” or an “event story.” Such a collection may be made available for a specified time period, such as the duration of an event to which the content relates. For example, content relating to a music concert may be made available as a “story” for the duration of that music concert. The collection management system 204 may also be responsible for publishing an icon that provides notification of the existence of a particular collection to the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curation interface 206 that allows a collection manager to manage and curate a particular collection of content. For example, the curation interface 206 enables an event organizer to curate a collection of content relating to a specific event (e.g., delete inappropriate content or redundant messages). Additionally, the collection management system 204 employs machine vision (or image recognition technology) and content rules to automatically curate a content collection. In certain examples, compensation may be paid to a user for the inclusion of user-generated content into a collection. In such cases, the collection management system 204 operates to automatically make payments to such users for the use of their content.

The augmentation system 208 provides various functions that enable a user to augment (e.g., annotate or otherwise modify or edit) media content associated with a message. For example, the augmentation system 208 provides functions related to the generation and publishing of media overlays for messages processed by the messaging system 100. The augmentation system 208 operatively supplies a media overlay or augmentation (e.g., an image filter) to the messaging client 104 based on a geolocation of the client device 102. In another example, the augmentation system 208 operatively supplies a media overlay to the messaging client 104 based on other information, such as social network information of the user of the client device 102. A media overlay may include audio and visual content and visual effects. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects. An example of a visual effect includes color overlaying. The audio and visual content or the visual effects can be applied to a media content item (e.g., a photo) at the client device 102. For example, the media overlay may include text or image that can be overlaid on top of a photograph taken by the client device 102. In another example, the media overlay includes an identification of a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay (e.g., Beach Coffee House). In another example, the augmentation system 208 uses the geolocation of the client device 102 to identify a media overlay that includes the name of a merchant at the geolocation of the client device 102. The media overlay may include other indicia associated with the merchant. The media overlays may be stored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-based publication platform that enables users to select a geolocation on a map and upload content associated with the selected geolocation. The user may also specify circumstances under which a particular media overlay should be offered to other users. The augmentation system 208 generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-based publication platform that enables merchants to select a particular media overlay associated with a geolocation via a bidding process. For example, the augmentation system 208 associates the media overlay of the highest bidding merchant with a corresponding geolocation for a predefined amount of time.

The map system 210 provides various geographic location functions and supports the presentation of map-based media content and messages by the messaging client 104. For example, the map system 210 enables the display of user icons or avatars (e.g., stored in profile data 316) on a map to indicate a current or past location of “friends” of a user, as well as media content (e.g., collections of messages including photographs and videos) generated by such friends, within the context of a map. For example, a message posted by a user to the messaging system 100 from a specific geographic location may be displayed within the context of a map at that particular location to “friends” of a specific user on a map interface of the messaging client 104. A user can furthermore share his or her location and status information (e.g., using an appropriate status avatar) with other users of the messaging system 100 via the messaging client 104, with this location and status information being similarly displayed within the context of a map interface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the context of the messaging client 104. The messaging client 104 provides a game interface providing a list of available games that can be launched by a user within the context of the messaging client 104 and played with other users of the messaging system 100. The messaging system 100 further enables a particular user to invite other users to participate in the play of a specific game, by issuing invitations to such other users from the messaging client 104. The messaging client 104 also supports both the voice and text messaging (e.g., chats) within the context of gameplay, provides a leaderboard for the games, and also supports the provision of in-game rewards (e.g., coins and items).

The external resource system 214 provides an interface for the messaging client 104 to communicate with remote servers (e.g., third-party servers 110) to launch or access external resources, i.e., applications or applets. Each third-party server 110 hosts, for example, a markup language (e.g., HTML5) based application or small-scale version of an application (e.g., game, utility, payment, or ride-sharing application). The messaging client 104 may launches a web-based resource (e.g., application) by accessing the HTML5 file from the third-party servers 110 associated with the web-based resource. In certain examples, applications hosted by third-party servers 110 are programmed in JavaScript leveraging a Software Development Kit (SDK) provided by the messaging server 118. The SDK includes Application Programming Interfaces (APIs) with functions that can be called or invoked by the web-based application. In certain examples, the messaging server 118 includes a JavaScript library that provides a given external resource access to certain user data of the messaging client 104. HTML5 is used as an example technology for programming games, but applications and resources programmed based on other technologies can be used.

In order to integrate the functions of the SDK into the web-based resource, the SDK is downloaded by a third-party server 110 from the messaging server 118 or is otherwise received by the third-party server 110. Once downloaded or received, the SDK is included as part of the application code of a web-based external resource. The code of the web-based resource can then call or invoke certain functions of the SDK to integrate features of the messaging client 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides the bridge between an external resource (e.g., applications 106 or applets and the messaging client 104. This provides the user with a seamless experience of communicating with other users on the messaging client 104, while also preserving the look and feel of the messaging client 104. To bridge communications between an external resource and a messaging client 104, in certain examples, the SDK facilitates communication between third-party servers 110 and the messaging client 104. In certain examples, a Web ViewJavaScriptBridge running on a client device 102 establishes two one-way communication channels between an external resource and the messaging client 104. Messages are sent between the external resource and the messaging client 104 via these communication channels asynchronously. Each SDK function invocation is sent as a message and callback. Each SDK function is implemented by constructing a unique callback identifier and sending a message with that callback identifier.

By using the SDK, not all information from the messaging client 104 is shared with third-party servers 110. The SDK limits which information is shared based on the needs of the external resource. In certain examples, each third-party server 110 provides an HTML5 file corresponding to the web-based external resource to the messaging server 118. The messaging server 118 can add a visual representation (such as a box art or other graphic) of the web-based external resource in the messaging client 104. Once the user selects the visual representation or instructs the messaging client 104 through a GUI of the messaging client 104 to access features of the web-based external resource, the messaging client 104 obtains the HTML5 file and instantiates the resources necessary to access the features of the web-based external resource.

The messaging client 104 presents a graphical user interface (e.g., a landing page or title screen) for an external resource. During, before, or after presenting the landing page or title screen, the messaging client 104 determines whether the launched external resource has been previously authorized to access user data of the messaging client 104. In response to determining that the launched external resource has been previously authorized to access user data of the messaging client 104, the messaging client 104 presents another graphical user interface of the external resource that includes functions and features of the external resource. In response to determining that the launched external resource has not been previously authorized to access user data of the messaging client 104, after a threshold period of time (e.g., 3 seconds) of displaying the landing page or title screen of the external resource, the messaging client 104 slides up (e.g., animates a menu as surfacing from a bottom of the screen to a middle of or other portion of the screen) a menu for authorizing the external resource to access the user data. The menu identifies the type of user data that the external resource will be authorized to use. In response to receiving a user selection of an accept option, the messaging client 104 adds the external resource to a list of authorized external resources and allows the external resource to access user data from the messaging client 104. In some examples, the external resource is authorized by the messaging client 104 to access the user data in accordance with an OAuth 2 framework.

The messaging client 104 controls the type of user data that is shared with external resources based on the type of external resource being authorized. For example, external resources that include full-scale applications (e.g., an application 106) are provided with access to a first type of user data (e.g., only two-dimensional avatars of users with or without different avatar characteristics). As another example, external resources that include small-scale versions of applications (e.g., web-based versions of applications) are provided with access to a second type of user data (e.g., payment information, two-dimensional avatars of users, three-dimensional avatars of users, and avatars with various avatar characteristics). Avatar characteristics include different ways to customize a look and feel of an avatar, such as different poses, facial features, clothing, and so forth.

The image stylization system 130 protects certain facial features during the face stylization process. Specifically, the image stylization preserves protected user features during transformation of a human face image to a target domain style. In some examples the image stylization system 130 operates within the context of the messaging client 104. In some examples, the image stylization system 130 may be supported by the application servers 114.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, which may be stored in the database 126 of the messaging server system 108, according to certain examples. While the content of the database 126 is shown to comprise a number of tables, it will be appreciated that the data could be stored in other types of data structures (e.g., as an object-oriented database).

The database 126 includes message data stored within a message table 302. This message data includes, for any particular one message, at least message sender data, message recipient (or receiver) data, and a payload. Further details regarding information that may be included in a message and included within the message data stored in the message table 302 is described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g., referentially) to an entity graph 308 and profile data 316. Entities for which records are maintained within the entity table 306 may include individuals, corporate entities, organizations, objects, places, events, and so forth. Regardless of entity type, any entity regarding which the messaging server system 108 stores data may be a recognized entity. Each entity is provided with a unique identifier, as well as an entity type identifier (not shown).

The entity graph 308 stores information regarding relationships and associations between entities. Such relationships may be social, professional (e.g., work at a common corporation or organization) interested-based or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about a particular entity. The profile data 316 may be selectively used and presented to other users of the messaging system 100, based on privacy settings specified by a particular entity. Where the entity is an individual, the profile data 316 includes, for example, a username, telephone number, address, settings (e.g., notification and privacy settings), as well as a user-selected avatar representation (or collection of such avatar representations). A particular user may then selectively include one or more of these avatar representations within the content of messages communicated via the messaging system 100, and on map interfaces displayed by messaging clients 104 to other users. The collection of avatar representations may include “status avatars,” which present a graphical representation of a status or activity that the user may select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group may similarly include one or more avatar representations associated with the group, in addition to the group name, members, and various settings (e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays or filters, in an augmentation table 310. The augmentation data is associated with and applied to videos (for which data is stored in a video table 304) and images (for which data is stored in an image table 312).

Filters, in one example, are overlays that are displayed as overlaid on an image or video during presentation to a recipient user. Filters may be of various types, including user-selected filters from a set of filters presented to a sending user by the messaging client 104 when the sending user is composing a message. Other types of filters include geolocation filters (also known as geo-filters), which may be presented to a sending user based on geographic location. For example, geolocation filters specific to a neighborhood or special location may be presented within a user interface by the messaging client 104, based on geolocation information determined by a Global Positioning System (GPS) unit of the client device 102.

Another type of filter is a data filter, which may be selectively presented to a sending user by the messaging client 104, based on other inputs or information gathered by the client device 102 during the message creation process. Examples of data filters include current temperature at a specific location, a current speed at which a sending user is traveling, battery life for a client device 102, or the current time.

Other augmentation data that may be stored within the image table 312 includes augmented reality content items (e.g., corresponding to applying Lenses or augmented reality experiences). An augmented reality content item may be a real-time special effect and sound that may be added to an image or a video.

As described above, augmentation data includes augmented reality content items, overlays, image transformations, AR images, and similar terms refer to modifications that may be applied to image data (e.g., videos or images). This includes real-time modifications, which modify an image as it is captured using device sensors (e.g., one or multiple cameras) of a client device 102 and then displayed on a screen of the client device 102 with the modifications. This also includes modifications to stored content, such as video clips in a gallery that may be modified. For example, in a client device 102 with access to multiple augmented reality content items, a user can use a single video clip with multiple augmented reality content items to see how the different augmented reality content items will modify the stored clip. For example, multiple augmented reality content items that apply different pseudorandom movement models can be applied to the same content by selecting different augmented reality content items for the content. Similarly, real-time video capture may be used with an illustrated modification to show how video images currently being captured by sensors of a client device 102 would modify the captured data. Such data may simply be displayed on the screen and not stored in memory, or the content captured by the device sensors may be recorded and stored in memory with or without the modifications (or both). In some systems, a preview feature can show how different augmented reality content items will look within different windows in a display at the same time. This can, for example, enable multiple windows with different pseudorandom animations to be viewed on a display at the same time.

Data and various systems using augmented reality content items or other such transform systems to modify content using this data can thus involve detection of objects (e.g., faces, hands, bodies, cats, dogs, surfaces, objects, etc.), tracking of such objects as they leave, enter, and move around the field of view in video frames, and the modification or transformation of such objects as they are tracked. In various examples, different methods for achieving such transformations may be used. Some examples may involve generating a three-dimensional mesh model of the object or objects and using transformations and animated textures of the model within the video to achieve the transformation. In other examples, tracking of points on an object may be used to place an image or texture (which may be two dimensional or three dimensional) at the tracked position. In still further examples, neural network analysis of video frames may be used to place images, models, or textures in content (e.g., images or frames of video). Augmented reality content items thus refer both to the images, models, and textures used to create transformations in content, as well as to additional modeling and analysis information needed to achieve such transformations with object detection, tracking, and placement.

Real-time video processing can be performed with any kind of video data (e.g., video streams, video files, etc.) saved in a memory of a computerized system of any kind. For example, a user can load video files and save them in a memory of a device or can generate a video stream using sensors of the device. Additionally, any objects can be processed using a computer animation model, such as a human's face and parts of a human body, animals, or non-living things such as chairs, cars, or other objects.

In some examples, when a particular modification is selected along with content to be transformed, elements to be transformed are identified by the computing device, and then detected and tracked if they are present in the frames of the video. The elements of the object are modified according to the request for modification, thus transforming the frames of the video stream. Transformation of frames of a video stream can be performed by different methods for different kinds of transformation. For example, for transformations of frames mostly referring to changing forms of object's elements characteristic points for each element of an object are calculated (e.g., using an Active Shape Model (ASM) or other known methods). Then, a mesh based on the characteristic points is generated for each of the at least one element of the object. This mesh used in the following stage of tracking the elements of the object in the video stream. In the process of tracking, the mentioned mesh for each element is aligned with a position of each element. Then, additional points are generated on the mesh. A first set of first points is generated for each element based on a request for modification, and a set of second points is generated for each element based on the set of first points and the request for modification. Then, the frames of the video stream can be transformed by modifying the elements of the object on the basis of the sets of first and second points and the mesh. In such method, a background of the modified object can be changed or distorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object using its elements can be performed by calculating characteristic points for each element of an object and generating a mesh based on the calculated characteristic points. Points are generated on the mesh, and then various areas based on the points are generated. The elements of the object are then tracked by aligning the area for each element with a position for each of the at least one element, and properties of the areas can be modified based on the request for modification, thus transforming the frames of the video stream. Depending on the specific request for modification properties of the mentioned areas can be transformed in different ways. Such modifications may involve changing color of areas; removing at least some part of areas from the frames of the video stream; including one or more new objects into areas which are based on a request for modification; and modifying or distorting the elements of an area or object. In various examples, any combination of such modifications or other similar modifications may be used. For certain models to be animated, some characteristic points can be selected as control points to be used in determining the entire state-space of options for the model animation.

In some examples of a computer animation model to transform image data using face detection, the face is detected on an image with use of a specific face detection algorithm (e.g., Viola-Jones). Then, an Active Shape Model (ASM) algorithm is applied to the face region of an image to detect facial feature reference points.

Other methods and algorithms suitable for face detection can be used. For example, in some examples, features are located using a landmark, which represents a distinguishable point present in most of the images under consideration. For facial landmarks, for example, the location of the left eye pupil may be used. If an initial landmark is not identifiable (e.g., if a person has an eyepatch), secondary landmarks may be used. Such landmark identification procedures may be used for any such objects. In some examples, a set of landmarks forms a shape. Shapes can be represented as vectors using the coordinates of the points in the shape. One shape is aligned to another with a similarity transform (allowing translation, scaling, and rotation) that minimizes the average Euclidean distance between shape points. The mean shape is the mean of the aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned to the position and size of the face determined by a global face detector is started. Such a search then repeats the steps of suggesting a tentative shape by adjusting the locations of shape points by template matching of the image texture around each point and then conforming the tentative shape to a global shape model until convergence occurs. In some systems, individual template matches are unreliable, and the shape model pools the results of the weak template matches to form a stronger overall classifier. The entire search is repeated at each level in an image pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a client device (e.g., the client device 102) and perform complex image manipulations locally on the client device 102 while maintaining a suitable user experience, computation time, and power consumption. The complex image manipulations may include size and shape changes, emotion transfers (e.g., changing a face from a frown to a smile), state transfers (e.g., aging a subject, reducing apparent age, changing gender), style transfers, graphical element application, and any other suitable image or video manipulation implemented by a convolutional neural network that has been configured to execute efficiently on the client device 102.

In some examples, a computer animation model to transform image data can be used by a system where a user may capture an image or video stream of the user (e.g., a selfie) using a client device 102 having a neural network operating as part of a messaging client 104 operating on the client device 102. The transformation system operating within the messaging client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein. The modification icons include changes that may be the basis for modifying the user's face within the image or video stream as part of the modification operation. Once a modification icon is selected, the transform system initiates a process to convert the image of the user to reflect the selected modification icon (e.g., generate a smiling face on the user). A modified image or video stream may be presented in a graphical user interface displayed on the client device 102 as soon as the image or video stream is captured, and a specified modification is selected. The transformation system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real-time or near real-time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured, and the selected modification icon remains toggled. Machine taught neural networks may be used to enable such modifications.

The graphical user interface, presenting the modification performed by the transform system, may supply the user with additional interaction options. Such options may be based on the interface used to initiate the content capture and selection of a particular computer animation model (e.g., initiation from a content creator user interface). In various examples, a modification may be persistent after an initial selection of a modification icon. The user may toggle the modification on or off by tapping or otherwise selecting the face being modified by the transformation system and store it for later viewing or browse to other areas of the imaging application. Where multiple faces are modified by the transformation system, the user may toggle the modification on or off globally by tapping or selecting a single face modified and displayed within a graphical user interface. In some examples, individual faces, among a group of multiple faces, may be individually modified, or such modifications may be individually toggled by tapping or selecting the individual face or a series of individual faces displayed within the graphical user interface.

A story table 314 stores data regarding collections of messages and associated image, video, or audio data, which are compiled into a collection (e.g., a story or a gallery). The creation of a particular collection may be initiated by a particular user (e.g., each user for which a record is maintained in the entity table 306). A user may create a “personal story” in the form of a collection of content that has been created and sent/broadcast by that user. To this end, the user interface of the messaging client 104 may include an icon that is user-selectable to enable a sending user to add specific content to his or her personal story.

A collection may also constitute a “live story,” which is a collection of content from multiple users that is created manually, automatically, or using a combination of manual and automatic techniques. For example, a “live story” may constitute a curated stream of user-submitted content from varies locations and events. Users whose client devices have location services enabled and are at a common location event at a particular time may, for example, be presented with an option, via a user interface of the messaging client 104, to contribute content to a particular live story. The live story may be identified to the user by the messaging client 104, based on his or her location. The end result is a “live story” told from a community perspective.

A further type of content collection is known as a “location story,” which enables a user whose client device 102 is located within a specific geographic location (e.g., on a college or university campus) to contribute to a particular collection. In some examples, a contribution to a location story may require a second degree of authentication to verify that the end user belongs to a specific organization or other entity (e.g., is a student on the university campus).

As mentioned above, the video table 304 stores video data that, in one example, is associated with messages for which records are maintained within the message table 302. Similarly, the image table 312 stores image data associated with messages for which message data is stored in the entity table 306. The entity table 306 may associate various augmentations from the augmentation table 310 with various images and videos stored in the image table 312 and the video table 304.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400, according to some examples, generated by a messaging client 104 for communication to a further messaging client 104 or the messaging server 118. The content of a particular message 400 is used to populate the message table 302 stored within the database 126, accessible by the messaging server 118. Similarly, the content of a message 400 is stored in memory as “in-transit” or “in-flight” data of the client device 102 or the application servers 114. A message 400 is shown to include the following example components:

-   -   message identifier 402: a unique identifier that identifies the         message 400.     -   message text payload 404: text, to be generated by a user via a         user interface of the client device 102, and that is included in         the message 400.     -   message image payload 406: image data, captured by a camera         component of a client device 102 or retrieved from a memory         component of a client device 102, and that is included in the         message 400. Image data for a sent or received message 400 may         be stored in the image table 312.     -   message video payload 408: video data, captured by a camera         component or retrieved from a memory component of the client         device 102, and that is included in the message 400. Video data         for a sent or received message 400 may be stored in the video         table 304.     -   message audio payload 410: audio data, captured by a microphone         or retrieved from a memory component of the client device 102,         and that is included in the message 400.     -   message augmentation data 412: augmentation data (e.g., filters,         stickers, or other annotations or enhancements) that represents         augmentations to be applied to message image payload 406,         message video payload 408, or message audio payload 410 of the         message 400. Augmentation data for a sent or received message         400 may be stored in the augmentation table 310.     -   message duration parameter 414: parameter value indicating, in         seconds, the amount of time for which content of the message         (e.g., the message image payload 406, message video payload 408,         message audio payload 410) is to be presented or made accessible         to a user via the messaging client 104.     -   message geolocation parameter 416: geolocation data (e.g.,         latitudinal and longitudinal coordinates) associated with the         content payload of the message. Multiple message geolocation         parameter 416 values may be included in the payload, each of         these parameter values being associated with respect to content         items included in the content (e.g., a specific image into         within the message image payload 406, or a specific video in the         message video payload 408).     -   message story identifier 418: identifier values identifying one         or more content collections (e.g., “stories” identified in the         story table 314) with which a particular content item in the         message image payload 406 of the message 400 is associated. For         example, multiple images within the message image payload 406         may each be associated with multiple content collections using         identifier values.     -   message tag 420: each message 400 may be tagged with multiple         tags, each of which is indicative of the subject matter of         content included in the message payload. For example, where a         particular image included in the message image payload 406         depicts an animal (e.g., a lion), a tag value may be included         within the message tag 420 that is indicative of the relevant         animal. Tag values may be generated manually, based on user         input, or may be automatically generated using, for example,         image recognition.     -   message sender identifier 422: an identifier (e.g., a messaging         system identifier, email address, or device identifier)         indicative of a user of the Client device 102 on which the         message 400 was generated and from which the message 400 was         sent.     -   message receiver identifier 424: an identifier (e.g., a         messaging system identifier, email address, or device         identifier) indicative of a user of the client device 102 to         which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 may be pointers to locations in tables within which content data values are stored. For example, an image value in the message image payload 406 may be a pointer to (or address of) a location within an image table 312. Similarly, values within the message video payload 408 may point to data stored within a video table 304, values stored within the message augmentations 412 may point to data stored in an augmentation table 310, values stored within the message story identifier 418 may point to data stored in a story table 314, and values stored within the message sender identifier 422 and the message receiver identifier 424 may point to user records stored within an entity table 306.

Time-Based Access Limitation Architecture

FIG. 5 is a schematic diagram illustrating an access-limiting process 500, in terms of which access to content (e.g., an ephemeral message 502, and associated multimedia payload of data) or a content collection (e.g., an ephemeral message group 504) may be time-limited (e.g., made ephemeral).

An ephemeral message 502 is shown to be associated with a message duration parameter 506, the value of which determines an amount of time that the ephemeral message 502 will be displayed to a receiving user of the ephemeral message 502 by the messaging client 104. In one example, an ephemeral message 502 is viewable by a receiving user for up to a maximum of 10 seconds, depending on the amount of time that the sending user specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier 424 are shown to be inputs to a message timer 510, which is responsible for determining the amount of time that the ephemeral message 502 is shown to a particular receiving user identified by the message receiver identifier 424. In particular, the ephemeral message 502 will only be shown to the relevant receiving user for a time period determined by the value of the message duration parameter 506. The message timer 510 is shown to provide output to a more generalized ephemeral timer system 202, which is responsible for the overall timing of display of content (e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within an ephemeral message group 504 (e.g., a collection of messages in a personal story, or an event story). The ephemeral message group 504 has an associated group duration parameter 508, a value of which determines a time duration for which the ephemeral message group 504 is presented and accessible to users of the messaging system 100. The group duration parameter 508, for example, may be the duration of a music concert, where the ephemeral message group 504 is a collection of content pertaining to that concert. Alternatively, a user (either the owning user or a curator user) may specify the value for the group duration parameter 508 when performing the setup and creation of the ephemeral message group 504.

Additionally, each ephemeral message 502 within the ephemeral message group 504 has an associated group participation parameter 512, a value of which determines the duration of time for which the ephemeral message 502 will be accessible within the context of the ephemeral message group 504. Accordingly, a particular ephemeral message group 504 may “expire” and become inaccessible within the context of the ephemeral message group 504, prior to the ephemeral message group 504 itself expiring in terms of the group duration parameter 508. The group duration parameter 508, group participation parameter 512, and message receiver identifier 424 each provide input to a group timer 514, which operationally determines, firstly, whether a particular ephemeral message 502 of the ephemeral message group 504 will be displayed to a particular receiving user and, if so, for how long. Note that the ephemeral message group 504 is also aware of the identity of the particular receiving user as a result of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overall lifespan of an associated ephemeral message group 504, as well as an individual ephemeral message 502 included in the ephemeral message group 504. In one example, each and every ephemeral message 502 within the ephemeral message group 504 remains viewable and accessible for a time period specified by the group duration parameter 508. In a further example, a certain ephemeral message 502 may expire, within the context of ephemeral message group 504, based on a group participation parameter 512. Note that a message duration parameter 506 may still determine the duration of time for which a particular ephemeral message 502 is displayed to a receiving user, even within the context of the ephemeral message group 504. Accordingly, the message duration parameter 506 determines the duration of time that a particular ephemeral message 502 is displayed to a receiving user, regardless of whether the receiving user is viewing that ephemeral message 502 inside or outside the context of an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove a particular ephemeral message 502 from the ephemeral message group 504 based on a determination that it has exceeded an associated group participation parameter 512. For example, when a sending user has established a group participation parameter 512 of 24 hours from posting, the ephemeral timer system 202 will remove the relevant ephemeral message 502 from the ephemeral message group 504 after the specified 24 hours. The ephemeral timer system 202 also operates to remove an ephemeral message group 504 when either the group participation parameter 512 for each and every ephemeral message 502 within the ephemeral message group 504 has expired, or when the ephemeral message group 504 itself has expired in terms of the group duration parameter 508.

In certain use cases, a creator of a particular ephemeral message group 504 may specify an indefinite group duration parameter 508. In this case, the expiration of the group participation parameter 512 for the last remaining ephemeral message 502 within the ephemeral message group 504 will determine when the ephemeral message group 504 itself expires. In this case, a new ephemeral message 502, added to the ephemeral message group 504, with a new group participation parameter 512, effectively extends the life of an ephemeral message group 504 to equal the value of the group participation parameter 512.

Responsive to the ephemeral timer system 202 determining that an ephemeral message group 504 has expired (e.g., is no longer accessible), the ephemeral timer system 202 communicates with the messaging system 100 (and, for example, specifically the messaging client 104) to cause an indicium (e.g., an icon) associated with the relevant ephemeral message group 504 to no longer be displayed within a user interface of the messaging client 104. Similarly, when the ephemeral timer system 202 determines that the message duration parameter 506 for a particular ephemeral message 502 has expired, the ephemeral timer system 202 causes the messaging client 104 to no longer display an indicium (e.g., an icon or textual identification) associated with the ephemeral message 502.

The image stylization system 130 is shown to include five sub-systems: an image collector system 602, a machine learning (ML) model 604, a dataset adjustment system 606, an image transformation system 608, and an image translation model 610.

The image collector system 602 collects sample images of the target domain style. In some examples, the image collector system 602 accesses the sample image of the target domain style from a database 126. Turning to FIG. 7 , the target domain style may include, for example, an anime image style (item 702) or a statue image style (item 704). It is to be understood that the target domain style may be any stylized domain that resemble human faces. In some examples, the target domain style does not include actual human face images.

The ML model 604 may be an unsupervised ML model. The ML model 604 generates paired images. The paired images include a first image that is a source image and a second image that is a stylized version of the source image according to a target domain style that retains protected features of the source image. In some examples the ML Model 604 includes a generative adversarial network (GAN).

Turning to FIG. 8 , in some examples, the ML model 604 receives a source image 802 as input. The ML model 604 can generate a stylized image 804 corresponding to the target domain style. Image 806 is an image generated by the ML model 604 when it is instructed to retain protected features of the source image 802 such as skin color, gender and eye shape. In some examples, the ML model 604 may be provided the source image 802 and the set of protected features as an input. When the ML model 604 is provided the source image 802 and the set of protected features as input, the ML model 604 generates the stylized image that retains the set of protected features, 806. The ML model 604 will thus output a pair of images (image 802 and image 806).

In another example, the ML model 604 receives source image 808. The ML model 604 can transform the source image 808 into a stylized image 810 that corresponds to a target domain style. The ML model 604 can further generate the image 812 when it is instructed to retain protected features of the source image 808, such as the skin tone. When the ML model 604 receives the protected features as input in addition to the source image 808, the ML model 604 outputs a pair of images (image 808 and image 812). In another example, the ML model 604 receives a source image 814. The ML model 604 can transform the source image 814 into a stylized image 814 that corresponds to target domain style. The ML model 604 can further generate the image 818 when it is instructed to retain protected features of the source image 814, such as the skin tone. When the ML model 604 receives the protected features as input in addition to the source image 814, the ML model 604 will output a pair of images (image 814 and image 818).

Turning to FIG. 9 , in another example, the ML model 604 receives input images as shown in column 902. Column 904 represent a first stylized versions of the corresponding input images generated by the ML model 604. Column 906 represents second stylized versions of the corresponding input images generated by the ML model 604, when the ML model 604 is instructed to preserve protected features from the images in column 902 such as ethnicity and gender. Column 908 represents third stylized versions of the corresponding input images generated by the ML model 604, when the ML model 604 further preserves protected features from the images in column 902, such as eye shape.

The dataset adjustment system 606 analyzes the set of paired images generated by the ML model 604 against a plurality of protected feature criteria. Each protected feature criterion is represented by an unbiased classifier for a protected feature. The unbiased classifier may be a neural network, a manual evaluation system, or a suitable algorithm.

The unbiased classifier for each protected feature may be represented by C. The source image may be represented by X and the translated image to the target domain style may be represented by G. The output of C may be represented as Q. G is fair across C if:

|Pr(C(x)=Q)−Pr(C(G(x))=Q)|<ε, where ε>=0

Thus, G should be almost invisible to the classifier, C. For every G, the dataset adjustment system 606 may use its own set of C. The set of C may include, but is not limited to gender, race, skin tone, and the presence of eyeglasses. G satisfies the protected feature criteria if it is fair across all selected classifiers.

The dataset adjustment system 606 generates an adjusted set of images by augmenting the accessed set of images corresponding to the target domain style with a set of new images corresponding to the target domain style based on the protected feature criteria. For example, if a classifier generates a determination that a pair of images is unfair (e.g., the stylized image failed to preserve a protected feature of the source image), the dataset adjustment system generates artificial images that represent the target domain style and protected features. In some examples, instead of generating new images, the dataset adjustment system 606 may generate the adjusted set of images by removing images from overrepresented categories. Assuming a source image dataset is represented as X and a target image dataset is represented as Y, a category Q is underrepresented if:

Pr(C(X)=Q)−Pr(C(Y)=Q)>ε

A category, Q is overrepresented if

Pr(C(Y)=Q)−Pr(C(X)=Q)>ε

The ML model 604 may regenerate paired images based on the augmented set of images generated by the dataset adjustment system 606. For example, the ML model 604 may be retrained on the augmented set of images generated by the dataset adjustment system 606. Thus, the ML model 604 may regenerate paired images that previously did not meet the protected feature criteria. Similarly, the dataset adjustment system 606 may generate the adjusted set of images until the protected feature criteria is satisfied across all classifiers.

For example, the dataset adjustment system 606 may determine that the protected feature criteria are not satisfied for darker skin tones. Thus, as shown in FIG. 10 , the dataset adjustment system 606 may artificially generate a set of images 1002 in the target domain style that have a darker skin tone. The newly generated images 1002 are added to the adjusted set of images.

The image transformation system 608 may be a machine learning model or any suitable algorithm. Based on the analysis of the protected feature criteria by the dataset adjustment system 606, the image transformation system 608 determines a set of image adjustments for the set of image pairs generated during the third stage. The image transformation system 608 performs the set of image adjustments on the adjusted set of images. The image transformation system 608 may perform these adjustments sequentially. The resultant images may be referred to as the transformed set of images. The set of image adjustments may include for example, image processing and image-to-image translations. Image processing on the images generated at the third stage may include color adjustment, image blending, morphing, and the like. Image-to-image translation methods may include stylization, emotion transfer, gender transfer, and the like. The image transformation system 608 may further filter out image pairs that do not meet the protected feature criteria. In some examples, the set of adjustments may be determined by the dataset adjustment system 606. In some examples, the set of adjustments may be determined by the image transformation system 608.

For example, in FIG. 11 , the images in column 1102 are source images and the images in column 1104 represent the corresponding stylized images generated by the ML model 604. The images in column 1106 illustrate skin color adjustments performed by the image transformation system 608. In another example, image 1108 is a source image. Image 1110 is a stylized image generated by the ML model 604. The image transformation system 608 generates a morphed source image 1112 and applies a transformation on image 1112 to generate a transformed morphed image 1114. In this way, the image transformation system 608 can preserve neutral face emotions.

Turning to FIG. 12 , in another example, the image transformation system 608 filters out images that did not preserve protected features such as gender or skin color. The images in column 1202 are source images, the images in column 1204 represent the stylized images of the corresponding source images as generated by the ML model 604, and the images in column 1206 are the transformed stylized images generated by the image transformation system 608.

As shown in FIG. 13 , the image transformation system 608 performs a blending transformation. In some examples, it is essential to preserve the background of the source image and stylize only the face in the source image. Thus, to accomplish that, the image transformation system 608 may perform background blending. The images in column 1302 are the source images, the images in column 1304 are the stylized images generated by the ML model 604, and the images in column 1306 are the transformed stylized images generated by the image transformation system 608 via background blending.

Turning to FIG. 14 , the image transformation system 608 splits the transformation into a geometry transformations and style transformations. For example, the image transformation system 608 may first perform style transformations on the image to achieve the look and feel of the target domain style, but then may perform geometry transformations for the nose area to retain nose shape. The images in column 1402 are the source images. The images in column 1404 are the style transformed images and the images in column 1406 are the geometry transformed images. The images in column 1404 and 1406 may be generated by the image transformation system 608.

The image translation model 610 may be a supervised machine learning model. The image translation model 610 is trained on the transformed set of images generated by the image transformation system 608. The image translation model 610 is thus trained to generate a stylized face image from an input face image. The stylized face image may correspond to a style from a target domain.

Although the described flowcharts can show operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed. A process may correspond to a method, a procedure, an algorithm, etc. The operations of methods may be performed in whole or in part, may be performed in conjunction with some or all of the operations in other methods, and may be performed by any number of different systems, such as the systems described herein, or any portion thereof, such as a processor included in any of the systems.

FIG. 15 is a flowchart of a method 1500 for generating stylized images that preserve protected facial features of a user, according to some example embodiments. In one example, the processor in an image stylization system 130, the processor in the client device 102, the processor in the messaging server system 108 or any combination thereof, can perform the operations in the method 1500.

At operation 1502, the image stylization system 130 accesses a set of images corresponding to a target domain style. In some examples, the target domain style is an anime image style. For example, the image stylization system 130 may use the image collector system 602 to access the set of images. Further details of the image collector system 602 are provided above in accordance with FIG. 6 .

At operation 1504, the image stylization system 130 generates a set of paired images using a first machine learning model. The first machine learning model transforms a first image to a second image. The first image may be a source image comprising a human face. The second image may correspond to the target domain style. The first machine learning model may be the ML model 604. Further details of the ML model 604 are provided above in accordance with FIG. 6 .

At operation 1506, the image stylization system 130 analyzes the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria. Each protected feature criterion of the plurality of protected feature criteria is an unbiased classifier for a protected feature. The protected feature is a facial feature that is selected to be preserved during transformation of the source image. For example, a user of the image stylization system 130 may associate this facial feature or protected feature with their identity. This protected feature is preserved in the transformed image (e.g., the second image). Analyzing the generated set of paired images may further include generating an adjusted set of images. For example, the adjusted set of images may be generated by augmenting the accessed set of images corresponding to the target domain style with a set of new images corresponding to the target domain style. The adjusted set of images may also be generated by removing paired images from the set of paired images. The removed paired images may fail to meet at least one protected feature criteria of the plurality of protected feature criteria. In some examples, the first machine learning model is retrained on the adjusted set of images. Operation 1506 may be performed by the dataset adjustment system 606. Further details of the dataset adjustment system 606 are provided above in accordance with FIG. 6 . Operations 1504 and 1506 may be repeated until each protected feature criterion of the plurality of protected feature criterion have been satisfied. In some examples, operation 1506 is performed using human labeling. It is to be understood that operation 1506 may be performed using any suitable method.

At operation 1508, based on the analysis, the image stylization system 130 determines a set of image transformations for the generated set of pairs. The set of image transformations may be determined by the image transformation system 608. In some examples, the set of image transformations may be determined by the dataset adjustment system 606. At operation 1508, the image stylization system 130 generates a transformed set of paired images. For example, the image stylization system 130 performs the set of image transformations on the set of paired images. The set of image transformations may comprise image processing, image-to-image translation, or any combination thereof. The transformed set of paired images may be generated by the image transformation system 608. Further details of the image transformation system 608 are provided above in accordance with FIG. 6 .

At operation 1512, the image stylization system 130 generates stylized images corresponding to the target domain style. The stylized images generated using a supervised image translation model trained on the transformed set of paired images. The supervised image translation model may be the image translation model 610. Further details of the image translation model 610 are provided above in accordance with FIG. 6 .

Machine Architecture

FIG. 16 is a diagrammatic representation of the machine 1600 within which instructions 1610 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1600 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 1610 may cause the machine 1600 to execute any one or more of the methods described herein. The instructions 1610 transform the general, non-programmed machine 1600 into a particular machine 1600 programmed to carry out the described and illustrated functions in the manner described. The machine 1600 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 1600 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 1600 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smartwatch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 1610, sequentially or otherwise, that specify actions to be taken by the machine 1600. Further, while only a single machine 1600 is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 1610 to perform any one or more of the methodologies discussed herein. The machine 1600, for example, may comprise the client device 102 or any one of a number of server devices forming part of the messaging server system 108. In some examples, the machine 1600 may also comprise both client and server systems, with certain operations of a particular method or algorithm being performed on the server-side and with certain operations of the particular method or algorithm being performed on the client-side.

The machine 1600 may include processors 1604, memory 1606, and input/output I/O components 1602, which may be configured to communicate with each other via a bus 1640. In an example, the processors 1604 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1608 and a processor 1612 that execute the instructions 1610. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 16 shows multiple processors 1604, the machine 1600 may include a single processor with a single-core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory 1606 includes a main memory 1614, a static memory 1616, and a storage unit 1618, both accessible to the processors 1604 via the bus 1640. The main memory 1606, the static memory 1616, and storage unit 1618 store the instructions 1610 embodying any one or more of the methodologies or functions described herein. The instructions 1610 may also reside, completely or partially, within the main memory 1614, within the static memory 1616, within machine-readable medium 1620 within the storage unit 1618, within at least one of the processors 1604 (e.g., within the Processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1600.

The I/O components 1602 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1602 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1602 may include many other components that are not shown in FIG. 16 . In various examples, the I/O components 1602 may include user output components 1626 and user input components 1628. The user output components 1626 may include visual components (e.g., a display such as a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The user input components 1628 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1602 may include biometric components 1630, motion components 1632, environmental components 1634, or position components 1636, among a wide array of other components. For example, the biometric components 1630 include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye-tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components 1632 include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope).

The environmental components 1634 include, for example, one or cameras (with still image/photograph and video capabilities), illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment.

With respect to cameras, the client device 102 may have a camera system comprising, for example, front cameras on a front surface of the client device 102 and rear cameras on a rear surface of the client device 102. The front cameras may, for example, be used to capture still images and video of a user of the client device 102 (e.g., “selfies”), which may then be augmented with augmentation data (e.g., filters) described above. The rear cameras may, for example, be used to capture still images and videos in a more traditional camera mode, with these images similarly being augmented with augmentation data. In addition to front and rear cameras, the client device 102 may also include a 360° camera for capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rear cameras (e.g., a primary camera as well as a depth-sensing camera), or even triple, quad or penta rear camera configurations on the front and rear sides of the client device 102. These multiple cameras systems may include a wide camera, an ultra-wide camera, a telephoto camera, a macro camera and a depth sensor, for example.

The position components 1636 include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components 1602 further include communication components 1638 operable to couple the machine 1600 to a network 1622 or devices 1624 via respective coupling or connections. For example, the communication components 1638 may include a network interface Component or another suitable device to interface with the network 1622. In further examples, the communication components 1638 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 1624 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1638 may detect identifiers or include components operable to detect identifiers. For example, the communication components 1638 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 1638, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1614, static memory 1616, and memory of the processors 1604) and storage unit 1618 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 1610), when executed by processors 1604, cause various operations to implement the disclosed examples.

The instructions 1610 may be transmitted or received over the network 1622, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components 1638) and using any one of several well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 1610 may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to-peer coupling) to the devices 1624.

Software Architecture

FIG. 17 is a block diagram 1700 illustrating a software architecture 1704, which can be installed on any one or more of the devices described herein. The software architecture 1704 is supported by hardware such as a machine 1702 that includes processors 1720, memory 1726, and I/O components 1738. In this example, the software architecture 1704 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture 1704 includes layers such as an operating system 1712, libraries 1710, frameworks 1708, and applications 1706. Operationally, the applications 1706 invoke API calls 1750 through the software stack and receive messages 1752 in response to the API calls 1750.

The operating system 1712 manages hardware resources and provides common services. The operating system 1712 includes, for example, a kernel 1714, services 1716, and drivers 1722. The kernel 1714 acts as an abstraction layer between the hardware and the other software layers. For example, the kernel 1714 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionalities. The services 1716 can provide other common services for the other software layers. The drivers 1722 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 1722 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., USB drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.

The libraries 1710 provide a common low-level infrastructure used by the applications 1706. The libraries 1710 can include system libraries 1718 (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 1710 can include API libraries 1724 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 1710 can also include a wide variety of other libraries 1728 to provide many other APIs to the applications 1706.

The frameworks 1708 provide a common high-level infrastructure that is used by the applications 1706. For example, the frameworks 1708 provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks 1708 can provide a broad spectrum of other APIs that can be used by the applications 1706, some of which may be specific to a particular operating system or platform.

In an example, the applications 1706 may include a home application 1736, a contacts application 1730, a browser application 1732, a book reader application 1734, a location application 1742, a media application 1744, a messaging application 1746, a game application 1748, and a broad assortment of other applications such as a third-party application 1740. The applications 1706 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 1706, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 1740 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 1740 can invoke the API calls 1750 provided by the operating system 1712 to facilitate functionality described herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device.

“Client device” refers to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may use to access a network.

“Communication network” refers to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other types of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.

“Component” refers to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various examples, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein. A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component”(or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering examples in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time. Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware components. In examples in which multiple hardware components are configured or instantiated at different times, communications between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented component” refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors 1004 or processor-implemented components. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some examples, the processors or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other examples, the processors or processor-implemented components may be distributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for a time-limited duration. An ephemeral message may be a text, an image, a video and the like. The access time for the ephemeral message may be set by the message sender. Alternatively, the access time may be a default setting or a setting specified by the recipient. Regardless of the setting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devices and media (e.g., a centralized or distributed database, and associated caches and servers) that store executable instructions, routines and data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks The terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term “signal medium” shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. 

What is claimed is:
 1. A method comprising: accessing a set of images corresponding to a target domain style; generating a set of paired images using a first machine learning model, the set of paired images comprising a first image corresponding to a source image and a second image corresponding to the target domain style; analyzing the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria; based on the analysis, determining a set of image transformations for the generated set of pairs; generating a transformed set of paired images, the generating comprising performing the set of image transformations on the set of paired images; and generating stylized images corresponding to the target domain style, the stylized images generated using a supervised image translation model trained on the transformed set of paired images.
 2. The method of claim 1, wherein the source image comprises a human face.
 3. The method of claim 1, wherein the set of image transformations comprises: image processing and image-to-image translation.
 4. The method of claim 1, wherein each protected feature criterion of the plurality of protected feature criteria is an unbiased classifier for a protected feature.
 5. The method of claim 4, wherein the protected feature is a feature of the first image that is preserved during transformation of the first image to the second image.
 6. The method of claim 1, wherein the target domain style is an anime image style.
 7. The method of claim 1, wherein analyzing the generated set of paired images further comprises: generating an adjusted set of images, the generating comprising, augmenting the accessed set of images corresponding to the target domain style with a set of new images corresponding to the target domain style.
 8. The method of claim 7, wherein generating the adjusted set of paired images further comprises: removing paired images from the set of paired images, wherein the removed paired images fail to meet at least one protected feature criteria of the plurality of protected feature criteria.
 9. The method of claim 7, wherein the first machine learning model is retrained on the adjusted set of images.
 10. A system comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the system to perform operations comprising: accessing a set of images corresponding to a target domain style; generating a set of paired images using a first machine learning model, the set of paired images comprising a first image corresponding to a source image and a second image corresponding to the target domain style; analyzing the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria; based on the analysis, determining a set of image transformations for the generated set of pairs; generating a transformed set of paired images, the generating comprising performing the set of image transformations on the set of paired images; and generating stylized images corresponding to the target domain style, the stylized images generated using a supervised image translation model trained on the transformed set of paired images.
 11. The system of claim 10, wherein the first image is a source image comprising a human face.
 12. The system of claim 10, wherein the set of image transformations comprises: image processing and image-to-image translation.
 13. The system of claim 10, wherein each protected feature criterion of the plurality of protected feature criteria is an unbiased classifier for a protected feature.
 14. The system of claim 13, wherein the protected feature is a feature of the first image that is preserved during transformation of the first image to the second image.
 15. The system of claim 13, wherein the protected feature is provided as input to the first machine learning model.
 16. The system of claim 10, wherein the target domain style is an anime image style.
 17. The system of claim 10, wherein analyzing the generated set of paired images further comprises: generating an adjusted set of images, the generating comprising, augmenting the accessed set of images corresponding to the target domain style with a set of new images corresponding to the target domain style.
 18. The system of claim 17, wherein generating the adjusted set of paired images further comprises: removing paired images from the set of paired images, wherein the removed paired images fail to meet at least one protected feature criteria of the plurality of protected feature criteria.
 19. The system of claim 17, wherein the first machine learning model is retrained on the adjusted set of images.
 20. A non-transitory processor-readable storage medium storing processor executable instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising: accessing a set of images corresponding to a target domain style; generating a set of paired images using a first machine learning model, the set of paired images comprising a first image corresponding to a source image and a second image corresponding to the target domain style; analyzing the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria; based on the analysis, determining a set of image transformations for the generated set of pairs; generating a transformed set of paired images, the generating comprising performing the set of image transformations on the set of paired images; and generating stylized images corresponding to the target domain style, the stylized images generated using a supervised image translation model trained on the transformed set of paired images. 